NCA-GENM Question 2
Single answerA team is developing a multimodal generative AI model that generates captions for images. During the training phase, the team uses a dataset containing labeled images and corresponding text descriptions. Which of the following algorithmic approaches would be most appropriate to train this model?
- A
Supervised Learning
- B
Unsupervised Learning
- C
Reinforcement Learning
- D
Self-supervised Learning
Show answer and explanation
Correct answer: A
Explanation
Supervised Learning is the correct approach for this scenario because the task involves mapping labeled input data (images) to specific outputs (text descriptions). The availability of labeled image-text pairs makes supervised learning the most effective method for training the model.
- A. Correct.
Supervised Learning involves training a model on labeled data, where the algorithm learns to map inputs (images) to outputs (text descriptions). This approach is ideal for tasks with clear input-output relationships, like generating captions for images.
- B. Incorrect.
Unsupervised Learning involves finding hidden patterns or structures in data that is not labeled. Since this task requires labeled image-text pairs, unsupervised learning is not appropriate.
- C. Incorrect.
Reinforcement Learning involves training an agent to make decisions by maximizing cumulative rewards in a dynamic environment. This technique is not suitable for generating captions from labeled datasets.
- D. Incorrect.
Self-supervised Learning involves using data to generate labels from within the dataset itself. While this is useful for pre-training, it is not the most suitable approach for this specific task, which relies on external labeled data.